Understanding personal protective behaviours and opportunities for interventions:¶

Results from a multi-method investigation of cross-sectional data¶

Kaisa Sauro, James Twose, Gjalt-Jorn Peters, Matti Heino & Nelli Hankonen¶

approach used here: CatBoost Regression¶

In [1]:
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import session_info

from sklearn.metrics import mean_squared_error
from sklearn.model_selection import KFold, GroupKFold, GroupShuffleSplit, RepeatedStratifiedKFold, RepeatedKFold
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.model_selection import RepeatedStratifiedKFold, cross_val_score, cross_validate
from sklearn.ensemble import BaggingClassifier, BaggingRegressor
from sklearn.model_selection import KFold
import pingouin as pg
from catboost import CatBoostRegressor, Pool
# import xgboost
import shap
shap.initjs()

/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
  from .autonotebook import tqdm as notebook_tqdm
In [2]:
if "jms_style_sheet" in plt.style.available:
    plt.style.use("jms_style_sheet")

# _ = sns.set_style("whitegrid")

Virtual Environments and Packages¶

In [3]:
session_info.show(req_file_name="notebook-requirements.txt",
      write_req_file=True) #add write_req_file=True to function to get requirements.txt file of packages used
Out[3]:
Click to view session information 
-----
catboost            1.0.6
matplotlib          3.5.1
numpy               1.21.5
pandas              1.4.2
pingouin            0.5.1
seaborn             0.11.2
session_info        1.0.0
shap                0.39.0
sklearn             1.0.2
-----
Click to view modules imported as dependencies 
PIL                         9.0.1
appnope                     0.1.2
asttokens                   NA
backcall                    0.2.0
beta_ufunc                  NA
binom_ufunc                 NA
bottleneck                  1.3.4
brotli                      NA
certifi                     2020.06.20
cffi                        1.15.0
charset_normalizer          2.0.4
cloudpickle                 2.0.0
colorama                    0.4.4
cycler                      0.10.0
cython_runtime              NA
dateutil                    2.8.2
debugpy                     1.5.1
decorator                   5.1.1
defusedxml                  0.7.1
entrypoints                 0.4
executing                   0.8.3
idna                        3.3
ipykernel                   6.9.1
ipython_genutils            0.2.0
jedi                        0.18.1
joblib                      1.1.0
jupyter_server              1.13.5
kiwisolver                  1.3.1
littleutils                 NA
llvmlite                    0.38.0
matplotlib_inline           NA
mkl                         2.4.0
mpl_toolkits                NA
nbinom_ufunc                NA
numba                       0.55.1
numexpr                     2.8.1
outdated                    0.2.1
packaging                   21.3
pandas_flavor               NA
parso                       0.8.3
pexpect                     4.8.0
pickleshare                 0.7.5
pkg_resources               NA
prompt_toolkit              3.0.20
ptyprocess                  0.7.0
pure_eval                   0.2.2
pydev_ipython               NA
pydevconsole                NA
pydevd                      2.6.0
pydevd_concurrency_analyser NA
pydevd_file_utils           NA
pydevd_plugins              NA
pydevd_tracing              NA
pygments                    2.11.2
pyparsing                   3.0.4
pytz                        2021.3
requests                    2.27.1
scipy                       1.7.3
setuptools                  61.2.0
six                         1.16.0
slicer                      NA
socks                       1.7.1
stack_data                  0.2.0
statsmodels                 0.13.2
tabulate                    0.8.9
threadpoolctl               2.2.0
tornado                     6.1
tqdm                        4.64.0
traitlets                   5.1.1
typing_extensions           NA
urllib3                     1.26.9
wcwidth                     0.2.5
xarray                      0.20.1
yaml                        6.0
zmq                         22.3.0

-----
IPython             8.2.0
jupyter_client      7.2.2
jupyter_core        4.9.2
jupyterlab          3.3.2
notebook            6.4.8
-----
Python 3.10.4 (main, Mar 31 2022, 03:38:35) [Clang 12.0.0 ]
macOS-10.16-x86_64-i386-64bit
-----
Session information updated at 2022-06-11 12:08

Read in data, show info and data head¶

In [4]:
df = pd.read_csv("data/shield_gjames_21-09-20_prepped.csv").drop("Unnamed: 0", axis=1)
In [5]:
df.head()
Out[5]:
id sampling_weight demographic_gender demographic_age demographic_4_areas demographic_8_areas demographic_higher_education behaviour_indoors_nonhouseholders behaviour_close_contact behaviour_quarantined ... intention_public_transport_recoded intention_indoor_meeting_recoded intention_restaurant_recoded intention_pa_recoded intention_composite behaviour_indoors_nonhouseholders_recoded behaviour_unmasked_recoded behavior_composite behavior_composite_recoded intention_behavior_composite
0 1 2.060959 2 60+ 2 7 0 2 5 2 ... 0 0 0 0 0 1.000000 0.000000 0.000000 0.000000 0.000000
1 2 1.784139 2 40-49 1 1 1 3 3 2 ... 0 1 1 1 3 0.785714 0.214286 0.168367 0.841837 1.920918
2 3 1.204000 1 60+ 1 2 1 4 4 2 ... 0 0 0 0 0 0.500000 0.214286 0.107143 0.535714 0.267857
3 4 2.232220 1 60+ 2 6 0 4 3 2 ... 0 2 0 2 4 0.500000 0.500000 0.250000 1.250000 2.625000
4 5 1.627940 2 18-29 1 3 0 6 3 2 ... 0 2 0 0 2 0.000000 0.214286 0.000000 0.000000 1.000000

5 rows × 106 columns

In [6]:
sdt_columns = df.filter(regex="sdt").columns.tolist()
In [7]:
drop_sdt = True
if drop_sdt:
    df=df.drop(sdt_columns, axis=1)
In [8]:
df.shape
Out[8]:
(2272, 87)

Specify the feature list, grouping variable, and specify the grouping variable as a categorical variable¶

In [9]:
target = "intention_behavior_composite"
In [10]:
df[target] = (df[target] - 10) * -1
In [11]:
features_list = df.filter(regex="^automaticity|attitude|^norms|^risk|^effective").columns.tolist()
In [12]:
meta_columns = ['Original position', 'Variable name', 'Label',
       'Item english translation ', 'Label short', 'Type', 'New variable name',
       'variable name helper',
       'Of primary interest as a predictor (i.e. feature)?', 'English lo-anchor',
       'English hi-anchor']
In [13]:
sheet_id = "1BEX4W8XRGnuDk4Asa_pdKij3EIZBvhSPqHxFrDjM07k"
sheet_name = "Variable_names"
url = f"https://docs.google.com/spreadsheets/d/{sheet_id}/gviz/tq?tqx=out:csv&sheet={sheet_name}"
meta_df = pd.read_csv(url).loc[:, meta_columns]
In [14]:
# pd.set_option("display.max_colwidth", 350)
# pd.set_option('display.expand_frame_repr', True)
# meta_df[["Item english translation ", "Label short", "New variable name"]] #use Label Short instead of Item english translation for relabelling the axes
In [15]:
# meta_df = pd.read_excel("metadata/questionLabels.xlsx").loc[:, meta_columns]
In [16]:
meta_list = df.filter(regex="^automaticity|attitude|^norms|^risk|^effective|^behaviour|^intention").columns.tolist()
In [17]:
pd.set_option("display.max_colwidth", 350)
pd.set_option('display.expand_frame_repr', True)
meta_df.loc[meta_df["New variable name"].isin(meta_list), ["Item english translation ", "Label short", "New variable name"]] #use Label Short instead of Item english translation for relabelling the axes
Out[17]:
Item english translation Label short New variable name
12 How often in the last 7 days have you been indoors with people outside your household so that it is not related to obligations? For example, meeting friends, visiting hobbies, non-essential shopping, or other activities that are not required for your work or other duties.\n Being indoors with people outside household behaviour_indoors_nonhouseholders
13 In the last 7 days, have you been in close contact with people outside your household? Direct contact means spending more than one minute less than two meters away from another person or touching (e.g., shaking hands) outdoors or indoors. Close contact behaviour_close_contact
14 Are you currently in quarantine or isolation due to an official instruction or order? (For example, because you are waiting for a corona test, have returned from abroad or been exposed to a coronavirus) Quarantine or isolation behaviour_quarantined
15 How often in the last 7 days were you in your free time without a mask indoors with people you don’t live with? Without a mask indoors with people outside household behaviour_unmasked
24 If in the next 7 days you go to visit the following indoor spaces and there are people outside your household, Are you going to wear a mask? Grocery store or other store\n Intention to wear a mask grocery store or other store intention_store
25 If in the next 7 days you go to visit the following indoor spaces and there are people outside your household, Are you going to wear a mask? Bus, train or other means of public transport Intention to wear a mask public transport intention_public_transport
26 If in the next 7 days you go to visit the following indoor spaces and there are people outside your household, Are you going to wear a mask? Meeting people outside your household indoors Intention to wear a mask meeting people outside indoors intention_indoor_meeting
27 If in the next 7 days you go to visit the following indoor spaces and there are people outside your household, Are you going to wear a mask? Cafe, restaurant or bar indoors Intention to wear a mask cafe, restaurant or bar intention_restaurant
28 If in the next 7 days you go to visit the following indoor spaces and there are people outside your household, Are you going to wear a mask? Indoor exercise Intention to wear a mask indoor exercise intention_pa
29 Taking a mask with you to a store or public transport, for example, has already become automatic for some and is done without thinking. For others, taking a mask with them is not automatic at all, but requires conscious thinking and effort. Is taking a mask with you automatic for you? automaticity_carry_mask
30 Putting on a mask, for example in a shop or on public transport, has already become automatic for some and it happens without thinking. For others, putting on a mask is not automatic at all, but requires conscious thinking and effort. Is putting on a mask automatic for you? automaticity_put_on_mask
32 What consequences do you think it has if you use a face mask in your free time? If or when I use a face mask… If or when I use a face mask… inst_attitude_protects_self
33 What consequences do you think it has if you use a face mask in your free time? If or when I use a face mask… If or when I use a face mask… inst_attitude_protects_others
34 What consequences do you think it has if you use a face mask in your free time? If or when I use a face mask… If or when I use a face mask… inst_attitude_sense_of_community
35 What consequences do you think it has if you use a face mask in your free time? If or when I use a face mask… If or when I use a face mask… inst_attitude_enough_oxygen
36 What consequences do you think it has if you use a face mask in your free time? If or when I use a face mask… If or when I use a face mask… inst_attitude_no_needless_waste
37 Who thinks you should use a face mask and who thinks not? In the following questions, by using a face mask, we mean holding a cloth or disposable face mask, surgical mask, or respirator on the face so that it covers the nose and mouth. The questions concern leisure time. My family and friends think I should .. \n My family and friends think I should .. norms_family_friends
38 People at risk think I should .. People at risk think I should .. norms_risk_groups
39 The authorities think I should .. The authorities think I should .. norms_officials
40 In the indoors spaces I visit, people on the site think I should… In the indoors spaces I visit, people on the site think I should… norms_people_present_indoors
41 When I use a face mask, I feel or would feel ... When I use a face mask, I feel or would feel ... aff_attitude_comfortable
42 When I use a face mask, I feel or would feel ... When I use a face mask, I feel or would feel ... aff_attitude_calm
43 When I use a face mask, I feel or would feel ... When I use a face mask, I feel or would feel ... aff_attitude_safe
44 When I use a face mask, I feel or would feel ... When I use a face mask, I feel or would feel ... aff_attitude_responsible
45 When I use a face mask, I feel or would feel ... When I use a face mask, I feel or would feel ... aff_attitude_difficult_breathing
61 If two unvaccinated people from different households meet indoors, what means do you think would be effective in preventing coronavirus infection? Hand washing and use of gloves Hand washing and use of gloves effective_means_handwashing
62 Using a face mask Using a face mask effective_means_masks
63 Keeping a safety distance (2 meters) Keeping a safety distance (2 meters) effective_means_distance
64 Ventilation Ventilation effective_means_ventilation
65 How likely do you think you will get a coronavirus infection in your free time in the next month? Perceived risk coronavirus infection risk_likely_contagion
66 How likely do you think you would get a coronavirus infection in your free time in the next month if you did nothing to protect yourself from it?\r Perceived risk coronavirus infection with no protective behaviours risk_contagion_absent_protection
67 If you got a coronavirus infection, how serious a threat would you rate it to your health?\r Perceived risk severity coronavirus infection risk_severity
68 Spread of coronavirus… Spread of coronavirus… risk_fear_spread
69 The fact that I would get infected myself .. I would get infected myself .. risk_fear_contagion_self
70 That my loved one would get infected... Loved one would get infected... risk_fear_contagion_others
71 Consequences of measures taken to prevent the spread of the coronavirus... Measures taken to prevent the spread risk_fear_restrictions
In [18]:
pd.set_option("display.max_colwidth", 100)

EDA on the target¶

Check the amount of samples in the target

In [19]:
_ = sns.violinplot(data=df[[target]].melt(), 
                    x="variable", 
                    y="value"
               )
_ = sns.stripplot(data=df[[target]].melt(), 
                    x="variable", 
                    y="value",
                  edgecolor='white',
                  linewidth=0.5
               )
In [20]:
pd.crosstab(df["demographic_gender"], df["demographic_age"])
Out[20]:
demographic_age 18-29 30-39 40-49 50-59 60+
demographic_gender
1 114 169 187 168 337
2 281 185 229 211 391
In [21]:
target_df = df[target]
target_df.describe().to_frame().T
Out[21]:
count mean std min 25% 50% 75% max
intention_behavior_composite 2272.0 8.582428 1.524704 -0.0 8.017857 8.964286 9.5 10.0
In [22]:
_ = plt.figure(figsize=(20, 5))
_ = sns.countplot(x=target_df)
_ = plt.xticks(rotation=90)

Force all feature variables to categorical data¶

In [23]:
df[features_list] = df[features_list].astype("category")
In [24]:
df = (df[["demographic_age", "demographic_higher_education"] + features_list + [target]])
In [25]:
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2272 entries, 0 to 2271
Data columns (total 30 columns):
 #   Column                            Non-Null Count  Dtype   
---  ------                            --------------  -----   
 0   demographic_age                   2272 non-null   object  
 1   demographic_higher_education      2272 non-null   int64   
 2   automaticity_carry_mask           2272 non-null   category
 3   automaticity_put_on_mask          2272 non-null   category
 4   inst_attitude_protects_self       2272 non-null   category
 5   inst_attitude_protects_others     2272 non-null   category
 6   inst_attitude_sense_of_community  2272 non-null   category
 7   inst_attitude_enough_oxygen       2272 non-null   category
 8   inst_attitude_no_needless_waste   2272 non-null   category
 9   norms_family_friends              2272 non-null   category
 10  norms_risk_groups                 2272 non-null   category
 11  norms_officials                   2272 non-null   category
 12  norms_people_present_indoors      2272 non-null   category
 13  aff_attitude_comfortable          2272 non-null   category
 14  aff_attitude_calm                 2272 non-null   category
 15  aff_attitude_safe                 2272 non-null   category
 16  aff_attitude_responsible          2272 non-null   category
 17  aff_attitude_difficult_breathing  2272 non-null   category
 18  effective_means_handwashing       2272 non-null   category
 19  effective_means_masks             2272 non-null   category
 20  effective_means_distance          2272 non-null   category
 21  effective_means_ventilation       2272 non-null   category
 22  risk_likely_contagion             2272 non-null   category
 23  risk_contagion_absent_protection  2272 non-null   category
 24  risk_severity                     2272 non-null   category
 25  risk_fear_spread                  2272 non-null   category
 26  risk_fear_contagion_self          2272 non-null   category
 27  risk_fear_contagion_others        2272 non-null   category
 28  risk_fear_restrictions            2272 non-null   category
 29  intention_behavior_composite      2272 non-null   float64 
dtypes: category(27), float64(1), int64(1), object(1)
memory usage: 122.7+ KB
In [26]:
grouping_var = target
In [27]:
display(df[grouping_var].value_counts().head().to_frame()), df.shape[0], df[grouping_var].value_counts().head().sum()
intention_behavior_composite
10.000000 424
9.500000 228
9.000000 187
8.885204 155
9.385204 112
Out[27]:
(None, 2272, 1106)
In [28]:
def naive_catboost_forest_summary(df: pd.DataFrame,
                                 grouping_var: str,
                                 column_list: list,
                                  plot_title: str
                                 ):
    y = df[grouping_var]
    X = df[column_list]

    feature_plot, ax = plt.subplots(figsize=(10,7))
    _ = sns.boxplot(ax=ax, 
                    data=X.apply(lambda x: x.cat.codes), 
                    orient="v", 
                   )
    _ = plt.title(f'Feature Distributions {plot_title}')
    _ = plt.setp(ax.get_xticklabels(), rotation=90)
    _ = plt.grid()
    _ = plt.tight_layout()
    _ = plt.show()

    model = CatBoostRegressor(iterations=500,
                               depth=None,
                               learning_rate=1,
                               loss_function='RMSE',
                               verbose=False)

    # train the model
    _ = model.fit(X, y, cat_features=column_list)

    # create dataframe with importances per feature
    feature_importance = pd.Series(dict(zip(column_list, model.feature_importances_.round(2)))) 

    feature_importance_df = pd.DataFrame(feature_importance.sort_values(ascending=False)).reset_index().rename(columns={"index": "feature", 0: "feature_importance"})

    _ = plt.figure(figsize=(7, 7))
    gini_plot = sns.barplot(data=feature_importance_df, 
                            x="feature_importance", 
                            y="feature")
    _ = plt.title(f'Feature Importance {plot_title}')
    _ = plt.show()

    shap_values = model.get_feature_importance(Pool(X, label=y,cat_features=X.columns.tolist()), type="ShapValues")

    shap_values = shap_values[:,:-1]

    _ = shap.summary_plot(shap_values, 
                                  X.astype(int), 
                                  feature_names=X.columns, 
                                  max_display=X.shape[1],
                                 show=False,
                         title=plot_title) 
    shap_plot = plt.gca()
    
    tmp_actual = (X
     .melt(value_name='actual_value')
    )

    tmp_shap = (pd.DataFrame(shap_values, columns=column_list)
     .melt(value_name='shap_value')
    )

    shap_actual_df = pd.concat([tmp_actual, tmp_shap[["shap_value"]]], axis=1)

    
    y_pred = model.predict(X)

    df_test = pd.DataFrame({"y_pred": y_pred, grouping_var: y})

    user_ids_first = df_test.head(1).index.tolist()[0]
    user_ids_last = df_test.tail(1).index.tolist()[0]

    _ = plt.figure(figsize=(30,8))
    _ = plt.title(f"Catboost Regressor(fitted set) | RMSE = {round(np.sqrt(mean_squared_error(df_test['y_pred'], df_test[grouping_var])),4)} | bias Error = {round(np.mean(df_test['y_pred'] - df_test[grouping_var]), 4)} | {plot_title}")
    rmse_plot = plt.stem(df_test.index, df_test['y_pred'] - df_test[grouping_var], use_line_collection=True, linefmt='grey', markerfmt='D')
    _ = plt.hlines(y=round(np.sqrt(mean_squared_error(df_test['y_pred'], df_test[grouping_var])),2), colors='b', linestyles='-.', label='+ RMSE', 
                   xmin = user_ids_first, 
                   xmax = user_ids_last
                  ) 
    _ = plt.hlines(y=round(-np.sqrt(mean_squared_error(df_test['y_pred'], df_test[grouping_var])),2), colors='b', linestyles='-.', label='- RMSE', 
                   xmin = user_ids_first, 
                   xmax = user_ids_last
                  ) 
    _ = plt.xticks(rotation=90, ticks=df_test.index)
    _ = plt.ylabel(f"'Error = y_predicted - {grouping_var}'")
    _ = plt.legend()
    _ = plt.show()
    
    return feature_plot, gini_plot.get_figure(), shap_plot.get_figure(), rmse_plot, feature_importance_df, shap_actual_df
In [29]:
# %%capture
feature_plot_0, gini_plot_0, shap_plot_0, rmse_plot_0, feature_importance_df_0, shap_values_0 = naive_catboost_forest_summary(df = df[df["demographic_age"].isin(['18-29', '30-39'])],
                                                                                   grouping_var = grouping_var,
                                                                                   column_list = features_list,
                                                                                                                                   plot_title="18 - 39"
                                                                                  )
In [30]:
feature_plot_1, gini_plot_1, shap_plot_1, rmse_plot_1, feature_importance_df_1, shap_values_1 = naive_catboost_forest_summary(df = df[df["demographic_age"].isin(['40-49', '50-59'])],
                                                                                   grouping_var = grouping_var,
                                                                                   column_list = features_list,
                                                                                                                                   plot_title="40 - 59"
                                                                                  )
In [31]:
feature_plot_2, gini_plot_2, shap_plot_2, rmse_plot_2, feature_importance_df_2, shap_values_2 = naive_catboost_forest_summary(df = df[df["demographic_age"].isin(['60+'])],
                                                                                   grouping_var = grouping_var,
                                                                                   column_list = features_list,
                                                                                                                                   plot_title="60+"
                                                                                  )
In [32]:
feature_plot_3, gini_plot_3, shap_plot_3, rmse_plot_3, feature_importance_df_3, shap_values_3 = naive_catboost_forest_summary(df = df,
                                                                                   grouping_var = grouping_var,
                                                                                   column_list = features_list,
                                                                                                                                   plot_title="All"
                                                                                  )
In [33]:
feature_plot_4, gini_plot_4, shap_plot_4, rmse_plot_4, feature_importance_df_4, shap_values_4 = naive_catboost_forest_summary(df = df[df[grouping_var]!=10],
                                                                                   grouping_var = grouping_var,
                                                                                   column_list = features_list,
                                                                                                                                   plot_title="All - No 10's in target"
                                                                                  )
In [34]:
feature_plot_5, gini_plot_5, shap_plot_5, rmse_plot_5, feature_importance_df_5, shap_values_5 = naive_catboost_forest_summary(df = df[df["demographic_higher_education"]==0],
                                                                                   grouping_var = grouping_var,
                                                                                   column_list = features_list,
                                                                                                                                   plot_title="Lower Education"
                                                                                  )
In [35]:
feature_plot_6, gini_plot_6, shap_plot_6, rmse_plot_6, feature_importance_df_6, shap_values_6 = naive_catboost_forest_summary(df = df[df["demographic_higher_education"]==1],
                                                                                   grouping_var = grouping_var,
                                                                                   column_list = features_list,
                                                                                                                                   plot_title="Higher Education"
                                                                                  )
In [36]:
fig, axs = plt.subplots(nrows=1,
                 ncols=4,
                 sharex=True,
                 sharey=False,
                       figsize=(30, 7),
                       gridspec_kw={'wspace': 0.75})
fi_dfs_list = [feature_importance_df_0, feature_importance_df_1, feature_importance_df_2, feature_importance_df_3]
fi_titles_list = ["18 - 39", "40 - 59", "60+", "All"]

for i in range(0, len(fi_dfs_list)):
    fi_df = fi_dfs_list[i]
    _ = sns.barplot(data=fi_df, 
                    x="feature_importance", 
                    y="feature",
                    ax=axs[i],
                    palette="rocket"
                    )
    _ = axs[i].set_title(fi_titles_list[i])
# _ = plt.show()
In [37]:
fi_dfs_list = [feature_importance_df_0, feature_importance_df_1, feature_importance_df_2, feature_importance_df_3]
fi_titles_list = ["18 - 39", "40 - 59", "60+", "All"]
for i in range(0, len(fi_dfs_list)):
    fi_dfs_list[i]["age_group"] = fi_titles_list[i]
In [38]:
_ = plt.figure(figsize=(7, 5))
_ = sns.barplot(
                data=pd.concat(fi_dfs_list, axis=0).groupby("age_group").head(5),
                x="feature_importance", 
                y="feature",
                hue="age_group",
                palette="rocket",
                dodge=True
                )
In [39]:
fis_df = pd.concat(fi_dfs_list, axis=1)
In [40]:
fis_df.head(5)
Out[40]:
feature feature_importance age_group feature feature_importance age_group feature feature_importance age_group feature feature_importance age_group
0 automaticity_put_on_mask 12.42 18 - 39 automaticity_put_on_mask 19.44 40 - 59 automaticity_carry_mask 10.24 60+ automaticity_carry_mask 10.10 All
1 norms_people_present_indoors 7.77 18 - 39 inst_attitude_sense_of_community 11.25 40 - 59 norms_people_present_indoors 6.76 60+ risk_severity 7.71 All
2 aff_attitude_safe 6.97 18 - 39 inst_attitude_protects_self 5.38 40 - 59 aff_attitude_comfortable 6.18 60+ effective_means_masks 6.89 All
3 risk_fear_spread 6.38 18 - 39 effective_means_distance 4.80 40 - 59 risk_severity 5.80 60+ norms_people_present_indoors 5.15 All
4 inst_attitude_no_needless_waste 6.32 18 - 39 risk_fear_restrictions 4.63 40 - 59 aff_attitude_safe 5.61 60+ effective_means_ventilation 4.49 All
In [41]:
fig, axs = plt.subplots(nrows=1,
                 ncols=4,
                 sharex=True,
                 sharey=False,
                       figsize=(30, 7),
                       gridspec_kw={'wspace': 0.75})
shap_dfs_list = [shap_values_0, shap_values_1, shap_values_2, shap_values_3]
shap_titles_list = ["18 - 39", "40 - 59", "60+", "All"]

for i in range(0, len(shap_dfs_list)):
    shap_df = shap_dfs_list[i]
    var_order = shap_df.groupby("variable").var().sort_values(by = "shap_value", ascending = False).index.tolist()
    _ = sns.stripplot(data=shap_df, 
                    x="shap_value", 
                    y="variable",
                    hue="actual_value",
                  order=var_order,
                    ax=axs[i],
                    )
    _ = axs[i].set_title(shap_titles_list[i])
In [42]:
tmp_df = df.reset_index(drop=True)

X = tmp_df[features_list[2:]]
y = tmp_df[grouping_var]
In [43]:
accuracies_list = list()
all_pred_test_df = pd.DataFrame()
all_cors_df = pd.DataFrame()
kfold = RepeatedKFold(n_splits=10, n_repeats=10, random_state=42)
fold_number = 1

model = CatBoostRegressor(iterations=500,
                               depth=None,
                               learning_rate=1,
                               loss_function='RMSE',
                               verbose=False)

# enumerate the splits and summarize the distributions
for train_ix, test_ix in kfold.split(X):
    # select rows
    train_X, test_X = X.loc[train_ix, :], X.loc[test_ix, :]
    train_y, test_y = y.loc[train_ix], y.loc[test_ix]
    # summarize train and test composition
    train_0, train_1 = len(train_y[train_y==0]), len(train_y[train_y==1])
    test_0, test_1 = len(test_y[test_y==0]), len(test_y[test_y==1])
    
    _ = model.fit(X = train_X, 
                y = train_y,
               cat_features=X.columns.tolist())
    
    pred_y = model.predict(test_X)
    _ = accuracies_list.append(np.sqrt(mean_squared_error(test_y, pred_y)))
    
    pred_test_df = pd.DataFrame({grouping_var: test_y,
                 "predict": pred_y,
                                "fold_number": f"fold_{fold_number}"})
    
    all_pred_test_df = pd.concat([all_pred_test_df, 
                             pred_test_df
                            ])

    corr_df = pg.corr(x=pred_test_df[grouping_var], 
            y=pred_test_df["predict"], 
            alternative='two-sided', 
            method='spearman', 
           )
    
    all_cors_df = pd.concat([all_cors_df, 
                             corr_df.assign(fold_number=f"fold_{fold_number}")
                            ])
    
    fold_number += 1
In [44]:
_ = plt.figure(figsize=(3,5))
_ = sns.boxplot(y = accuracies_list)
_ = sns.swarmplot(y = accuracies_list, edgecolor="white", linewidth=1)
_ = plt.title("RMSE Cat Boost\nRegressor kfold cross validation")
In [45]:
pd.DataFrame(accuracies_list).describe().T
Out[45]:
count mean std min 25% 50% 75% max
0 100.0 1.460889 0.107116 1.172973 1.387053 1.45378 1.542548 1.713833
In [46]:
_ = sns.lmplot(data=all_pred_test_df, 
               x=grouping_var, 
               y="predict", 
               hue="fold_number",
              legend=False)
In [47]:
# ax = sns.jointplot(data=all_pred_test_df, 
#                   x=grouping_var, 
#                   y="predict", 
#                   hue="fold_number",
# #                   kind="reg",
#                    legend=False
#                  )
# # _ = ax._legend.remove()
In [48]:
all_cors_df.groupby("fold_number").mean().sort_values(by="r", ascending=False).round(3)
Out[48]:
n r p-val power
fold_number
fold_58 227.0 0.495 0.000 1.000
fold_12 228.0 0.485 0.000 1.000
fold_98 227.0 0.458 0.000 1.000
fold_10 227.0 0.458 0.000 1.000
fold_79 227.0 0.445 0.000 1.000
... ... ... ... ...
fold_19 227.0 0.223 0.001 0.924
fold_65 227.0 0.217 0.001 0.911
fold_61 228.0 0.206 0.002 0.881
fold_35 227.0 0.200 0.003 0.859
fold_100 227.0 0.177 0.007 0.766

100 rows × 4 columns

In [49]:
all_cors_df.describe()
Out[49]:
n r p-val power
count 100.000000 100.000000 1.000000e+02 100.000000
mean 227.200000 0.337624 1.474281e-04 0.989853
std 0.402015 0.063481 8.058543e-04 0.031872
min 227.000000 0.177255 1.944096e-15 0.766244
25% 227.000000 0.293919 4.683890e-09 0.995085
50% 227.000000 0.338370 1.743040e-07 0.999554
75% 227.000000 0.376328 6.681203e-06 0.999965
max 228.000000 0.495058 7.426213e-03 1.000000
In [50]:
_ = sns.boxplot(data=all_cors_df[["r", "p-val"]].melt(),
                x="variable", y="value")
_ = plt.axhline(y=0.05, c="grey", ls="--")
In [51]:
relabel_column = "Label short" # "Item english translation "

sort_shap_list = (pd.merge(shap_values_3
         .assign(shap_value=lambda d: d[["shap_value"]].abs())
         .groupby("variable")
         .mean()
         .sort_values(by="shap_value", ascending=False), 
         meta_df.loc[meta_df["New variable name"].isin(features_list), [relabel_column, "New variable name"]],
         left_index=True,
         right_on="New variable name")
 .set_index(["New variable name", relabel_column])
 .index
 .tolist()
#  .drop("New variable name", axis=1)
)
In [52]:
sort_shap_long_list = (pd.merge(shap_values_3
         .assign(shap_value=lambda d: d[["shap_value"]].abs())
         .groupby("variable")
         .mean()
         .sort_values(by="shap_value", ascending=False), 
         meta_df.loc[meta_df["New variable name"].isin(features_list), [relabel_column, "New variable name", 'English lo-anchor', 'English hi-anchor']],
         left_index=True,
         right_on="New variable name")
 .set_index(["New variable name", relabel_column, 'English lo-anchor', 'English hi-anchor'])
 .index
 .tolist()
#  .drop("New variable name", axis=1)
)
In [53]:
pd.Series([x[1] for x in sort_shap_list])
Out[53]:
0                          Perceived risk severity coronavirus infection
1      In the indoors spaces I visit, people on the site think I should…
2                           Is taking a mask with you automatic for you?
3                                                      Using a face mask
4                       When I use a face mask, I feel or would feel ...
5                                   Measures taken to prevent the spread
6     Perceived risk coronavirus infection with no protective behaviours
7                                          If or when I use a face mask…
8                                          If or when I use a face mask…
9                       When I use a face mask, I feel or would feel ...
10                                                           Ventilation
11                      When I use a face mask, I feel or would feel ...
12                                        I would get infected myself ..
13                                        Hand washing and use of gloves
14                                       Loved one would get infected...
15                                         If or when I use a face mask…
16                               Is putting on a mask automatic for you?
17                                         If or when I use a face mask…
18                               My family and friends think I should ..
19                                      People at risk think I should ..
20                                         If or when I use a face mask…
21                                                Spread of coronavirus…
22                      When I use a face mask, I feel or would feel ...
23                                     The authorities think I should ..
24                                  Keeping a safety distance (2 meters)
25                      When I use a face mask, I feel or would feel ...
26                                  Perceived risk coronavirus infection
dtype: object
In [54]:
def naive_catboost_shap(df: pd.DataFrame,
                                 grouping_var: str,
                                 column_list: list,
                                  plot_title: str,
                        max_display: int
                                 ):
    y = df[grouping_var]
    X = df[column_list]

    model = CatBoostRegressor(iterations=500,
                               depth=None,
                               learning_rate=1,
                               loss_function='RMSE',
                               verbose=False)

    # train the model
    _ = model.fit(X, y, cat_features=column_list)

    shap_values = model.get_feature_importance(Pool(X, label=y,cat_features=X.columns.tolist()), type="ShapValues")

    shap_values = shap_values[:,:-1]

    _ = shap.summary_plot(shap_values, 
                                  X.astype(int), 
                                  feature_names=X.columns, 
                                  max_display=max_display,
                                 show=False,
                         title=plot_title) 
    shap_plot = plt.gca()
    return shap_plot.get_figure()
In [55]:
display_length = 10
In [56]:
short_shap_plot_all = naive_catboost_shap(df = df,
                    grouping_var = grouping_var,
                    column_list = features_list,
                    plot_title="All",
                   max_display=display_length)
In [57]:
new_axis_list = pd.Series([f"{x[1]}: [{x[2]} - {x[3]}]" for x in sort_shap_long_list[:display_length]]).str.wrap(61).tolist()
# new_axis_list = pd.Series([f"({x[0]}) {x[1]}: [{x[2]} - {x[3]}]" for x in sort_shap_long_list[:display_length]]).str.wrap(61).tolist()
new_axis_list.reverse()
# new_axis_list = pd.Series([x[1] for x in sort_shap_list[:display_length]]).str.wrap(61).tolist()
_ = short_shap_plot_all.gca().set_yticklabels(new_axis_list, fontsize=11)
In [58]:
short_shap_plot_all.set_figheight(6)
short_shap_plot_all.set_figwidth(8)
short_shap_plot_all
Out[58]:
In [59]:
display_length = df.shape[0]
In [60]:
short_shap_plot_all = naive_catboost_shap(df = df,
                    grouping_var = grouping_var,
                    column_list = features_list,
                    plot_title="All",
                   max_display=display_length)
In [61]:
new_axis_list = pd.Series([f"{x[1]}: [{x[2]} - {x[3]}]" for x in sort_shap_long_list[:display_length]]).str.wrap(80).tolist()
new_axis_list = pd.Series([f"({x[0]}) {x[1]}: [{x[2]} - {x[3]}]" for x in sort_shap_long_list[:display_length]]).str.wrap(80).tolist()
new_axis_list.reverse()
# new_axis_list = pd.Series([x[1] for x in sort_shap_list[:display_length]]).str.wrap(61).tolist()
_ = short_shap_plot_all.gca().set_yticklabels(new_axis_list, fontsize=11)
In [62]:
short_shap_plot_all.set_figheight(18)
short_shap_plot_all.set_figwidth(8)
short_shap_plot_all
Out[62]:
In [63]:
!jupyter nbconvert --to html catboost_regression_clean.ipynb

[NbConvertApp] Converting notebook catboost_regression_clean.ipynb to html
[NbConvertApp] Writing 9655429 bytes to catboost_regression_clean.html